LuAna AI help to detect problems on chest X-rays
Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays - LungAnalysis (LuAna): LuAna Stepped Wedge Trial
We will test whether using LuAna, an AI tool, helps doctors find chest X-ray problems more often in adults with respiratory symptoms.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 1470 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Hospital Israelita Albert Einstein Academic / other |
| Locations | 1 site (Curitiba, Paraná) |
| Trial ID | NCT06686251 on ClinicalTrials.gov |
What this trial studies
This cluster-randomized, stepped-wedge trial compares usual physician interpretation of chest X-rays to physician interpretation assisted by LuAna, an AI diagnostic support app developed by Hospital Israelita Albert Einstein. Adults presenting with respiratory complaints who have at least one frontal chest X-ray taken at participating public health centers are included, while images from trauma, pre-operative assessment, lung cancer screening, poor-quality films, or without frontal views are excluded. Sites crossover from control to intervention according to the stepped-wedge schedule so that all clusters eventually use the AI tool, and outcomes are measured as detection rates of predefined radiographic findings such as consolidation, pleural effusion, pneumothorax, lung injury, and cardiomegaly. The trial is multicenter within the public health system and aims to validate LuAna across a diverse demographic before broader implementation.
Who should consider this trial
Good fit: Ideal candidates are adults (18+) who have frontal chest X-rays taken because of respiratory symptoms and are treated at participating public health centers.
Not a fit: Patients whose X-rays were taken for trauma, pre-operative evaluation, lung cancer screening, those with poor-quality images, or without a frontal view are unlikely to benefit from this intervention.
Why it matters
Potential benefit: If successful, LuAna could increase detection of important chest X-ray findings, speed up diagnosis, and improve patient safety and timeliness of care.
How similar studies have performed: Previous research on AI tools for chest x-ray interpretation has shown promising improvements in detection in some settings but results have varied and large-scale, real-world validation remains limited.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Non-reported chest X-rays (XRts) of individuals aged over 18 years. * Individuals images with respiratory complaints. * Chest X-rays taken during the presence of these respiratory symptoms or while being followed up for respiratory disease. * Chest X-rays taken on any X-ray machine. * Chest X-rays that include at least one frontal view of the chest. Exclusion Criteria: * Those whose chest X-ray was performed due to a history of trauma, pre-operative risk assessment, lung cancer screening, or exclusively for verifying the correct positioning of a peripheral intravenous catheter (PICC). * Chest X-rays with technical quality below the minimum required for proper interpretation and diagnosis. * Cases without at least one frontal view. * X-rays printed on regular paper.
Where this trial is running
Curitiba, Paraná
- FEAS Curitiba — Curitiba, Paraná, Brazil (Recruiting)
Study contacts
- Principal investigator: Joselisa Paiva, PhD — Hospital Israelita Albert Einstein
- Study coordinator: Joselisa Paiva, PhD
- Email: joselisa.paiva@einstein.br
- Phone: +55 (11) 9981667340
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.